One way for an end user to manage media data, such as, for example audio recordings, is to classify the recordings across one or more characteristics. Classification of the recording across one or more characteristics provides for efficient categorization, access, retrieval, or use of an audio recording. Conventional characteristics such as genre and release date continue to play an important part in performing these tasks. However, many conventional characteristics often do not provide enough detail and dimension to the description of a recording to perform dynamic tasks such as suggesting, recommending, or matching two similar audio or other recordings.
The “mood” that a user is likely to perceive when experiencing media data, such as visual data (e.g. a digital image), video recording, or audio recording, can be useful when a user seeks to perform dynamic tasks, such as those examples identified above. The mood associated with media data may describe the inherent feeling or emotion of the recording, and/or the feeling or emotion perceived, experienced or evoked in the listener or viewer. For example, a rich mood profile which may be associated with a piece of audio data can be used to find pieces of audio data (e.g. recordings) with congruent moods.